The quality provided by digital technologies has created a huge demand for digital products in general. Part of this digital revolution is the increased popularity of digital images. It is now possible to use a digital camera to capture an image and reproduce the image on some sort of display media using a personal computer (PC) monitor or high resolution printer. It has even become common practice to incorporate digital images into “web pages” available over a network such as the Internet and World Wide Web, or to send a digital image to another PC via electronic mail.
At the heart of this digital image revolution are image processing systems. These systems process the captured digital image to enhance the clarity and details of the image using sophisticated image processing algorithms. The use of these algorithms result in images that are substantially more accurate and detailed than previously achieved using older analog methods.
However, when compared to the direct observation of scenes, color images in general have two major limitations due to scene lighting conditions. First, the images captured and displayed by photographic and electronic cameras suffer from a comparative loss of detail and color in shadowed zones. This is known as the dynamic range problem. Second, the images are subject to color distortions when the spectral distribution of the illuminant changes. This is known as the color constancy problem. A commonly encountered instance of the color constancy problem is the spectral difference between daylight and artificial (e.g., tungsten) light which often is sufficiently strong.
Since human vision does not suffer from these various imaging drawbacks, it is reasonable to attempt to model machine vision based on human vision. A theory of human vision centered on the concept of a center/surround retinex was introduced by Edwin Land in “An Alternative Technique for the Computation of the Designator in the Retinex Theory of Color Vision,” Proceedings of the National Academy of Science, Volume 83, pp. 3078–3080, 1986. Land drew upon his earlier retinex concepts disclosed in “Color Vision and The Natural Image,” Proceedings of the National Academy of Science, Volume 45, pp. 115–129, 1959, but harmonized these with certain findings of the neurophysiology of vision. All of the retinex concepts were intended to be models for human color perception. The earlier retinex concepts involved “random walks” across image space and the resetting of the computation when color boundaries were crossed. Land's 1986 retinex concept of human vision was proposed as a center/surround spatial computation where the center was 2–4 arc-minutes in diameter and the surround was an inverse square function with a diameter of about 200–250 times that of the center.
Applications of Land's human vision theories to image processing resulted in several versions of the retinex algorithm. One recent version of the retinex algorithm is a computer-based version described by John McCann in “Lessons Learned from Mondrians Applied to Real Images and Color Gamuts,” Proceedings of IS&T/SID Seventh Color Imaging Conference, pp. 1–8, 1999. This version of the retinex algorithm (referred to herein as McCann '99 retinex) operates by creating a multi-resolution hierarchy from the input image data and computing lightness at each level of the hierarchy. Specifically, McCann '99 retinex begins lightness computations at the top level (i.e., the most highly averaged level of the hierarchy). After lightness values are computed at a reduced resolution, they are propagated down, by pixel replication, to a next level of the hierarchy as initial lightness estimates at that level. Further pixel comparisons refine the lightness estimates at the higher resolution level and then those new lightness estimates are again propagated down a level in the hierarchy. This process continues until computations are completed for the hierarchy's bottom level.
Although the McCann '99 retinex involves more computationally efficient spatial comparisons than a majority of its predecessors, its speed is still relatively slow and it fails to provide control over diffusion properties of the image.